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Background Since the global epidemic of the coronavirus disease 2019 (COVID-19), a large number of immunological studies related to COVID-19 have been published in various immunology journals. However, the results from these studies were discrete, and no study summarized the important immunological information about COVID-19 released by these immunology journals. This study aimed to comprehensively summarize the knowledge structure and research hotspots of COVID-19 published in major immunology journals through bibliometrics. Methods Publications on COVID-19 in major immunology journals were obtained from the Web of Science Core Collection. CiteSpace, VOSviewer, and R-bibliometrix were comprehensively used for bibliometric and visual analysis. Results 1,331 and 5,000 publications of 10 journals with high impact factors and 10 journals with the most papers were included, respectively. The USA, China, England, and Italy made the most significant contributions to these papers. University College London, National Institute of Allergy and Infectious Diseases, Harvard Medical School, University California San Diego, and University of Pennsylvania played a central role in international cooperation in the immunology research field of COVID-19. Yuen Kwok Yung was the most important author in terms of the number of publications and citations, and the H-index. CLINICAL INFECTIOUS DISEASES and FRONTIERS IN IMMUNOLOGY were the most essential immunology journals. These immunology journals mostly focused on the following topics: “Delta/Omicron variants”, “cytokine storm”, “neutralization/neutralizing antibody”, “T cell”, “BNT162b2”, “mRNA vaccine”, “vaccine effectiveness/safety”, and “long COVID”. Conclusion This study systematically uncovered a holistic picture of the current research on COVID-19 published in major immunology journals from the perspective of bibliometrics, which will provide a reference for future research in this field.
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Stored only when both papers are in the corpus — 9 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.